A statistically-augmented computational platform for evaluating meniscal function. Issue 8 (1st June 2015)

• Record Type: Journal Article
• Authors: Guo, Hongqiang; Santner, Thomas J.; Chen, Tony; Wang, Hongsheng; Brial, Caroline; Gilbert, Susannah L.; Koff, Matthew F.; Lerner, Amy L.; Maher, Suzanne A.
• Is Part Of: Journal of biomechanics. Volume 48:Issue 8(2015)
• Page Start: 1444
• Page End: 1453
• Publication Date: 2015-06-01
• Subjects: Meniscal replacement -- Finite element analysis -- Statistical analysis -- Knee -- Sensitivity; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2015.02.031
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Meniscal implants have been developed in an attempt to provide pain relief and prevent pathological degeneration of articular cartilage. However, as yet there has been no systematic and comprehensive analysis of the effects of the meniscal design variables on meniscal function across a wide patient population, and there are no clear design criteria to ensure the functional performance of candidate meniscal implants. Our aim was to develop a statistically-augmented, experimentally-validated, computational platform to assess the effect of meniscal properties and patient variables on knee joint contact mechanics during the activity of walking. Our analysis used Finite Element Models (FEMs) that represented the geometry, kinematics as based on simulated gait and contact mechanics of three laboratory tested human cadaveric knees. The FEMs were subsequently programmed to represent prescribed meniscal variables (circumferential and radial/axial moduli— E cm, E rm, stiffness of the meniscal attachments— S lpma, S lamp ) and patient variables (varus/valgus alignment—VVA, and articular cartilage modulus— E c ). The contact mechanics data generated from the FEM runs were used as training data to a statistical interpolator which estimated joint contact data for untested configurations of input variables. Our data suggested that while E cm and E rm of a meniscus are critical in determining knee joint mechanics in early and late stance (peak 1 and peak 3 of the gait cycle), for some knees that have greater laxity in the mid-stance phase of gait, the stiffness of the articular cartilage, E c, can influence force distribution across the tibial plateau. We found that the medial meniscus plays a dominant load-carrying role in the early stance phase and less so in late stance, while the lateral meniscus distributes load throughout gait. Joint contact mechanics in the medial compartment are more sensitive to E cm than those in the lateral compartment. Finally, throughout stance, varus–valgus alignment can overwhelm these relationships while the stiffness of meniscal attachments in the range studied have minimal effects on the knee joint mechanics. In summary, our statistically-augmented, computational platform allowed us to study how meniscal implant design variables (which can be controlled at the time of manufacture or implantation) interact with patient variables (which can be set in FEMs but cannot be controlled in patient studies) to affect joint contact mechanics during the activity of simulated walking.